LEADER 00991cam0-22003491i-450- 001 990000787910403321 005 20121228113435.0 010 $a88-207-2672-6 035 $a000078791 035 $aFED01000078791 035 $a(Aleph)000078791FED01 035 $a000078791 100 $a20020821d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aSkenographia$enote di estetica sull' architettura della scena$fClara Fiorillo 210 $aNapoli$cLiguori$d1996 215 $a239 p., [3] c. di tav. ripieg.$cill$d22 cm 610 0 $aTEATRO 610 0 $aSCENOGRAFIA 700 1$aFiorillo,$bClara$f<1953- >$0334247 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000787910403321 952 $aART.VI B 44$b10836$fFARBC 952 $aART.VI B 95$b8391$fFARBC 952 $a412015$b5777$fDCATA 952 $aART.VI B 94$b8204$fFARBC 959 $aFARBC 959 $aDCATA 996 $aSkenographia$9350603 997 $aUNINA LEADER 03646nam 22005295 450 001 9910502618603321 005 20251225180419.0 010 $a3-030-88552-6 024 7 $a10.1007/978-3-030-88552-6 035 $a(CKB)4100000012037908 035 $a(MiAaPQ)EBC6737923 035 $a(Au-PeEL)EBL6737923 035 $a(OCoLC)1272989641 035 $a(PPN)25805204X 035 $a(BIP)81776933 035 $a(BIP)81480639 035 $a(DE-He213)978-3-030-88552-6 035 $a(EXLCZ)994100000012037908 100 $a20210929d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Medical Image Reconstruction $e4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings /$fedited by Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (147 pages) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12964 300 $aIncludes index. 311 08$a3-030-88551-8 327 $aDeep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- Efficient Image Registration Network For Non-Rigid Cardiac Motion Estimation -- Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge -- Self-Supervised Dynamic MRI Reconstruction -- A Simulation Pipeline to Generate Realistic Breast Images For Learning DCE-MRI Reconstruction -- Deep MRI Reconstruction with Generative Vision Transformers -- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans -- One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline -- Physics-informed self-supervised deep learning reconstruction for accelerated rst-pass perfusion cardiac MRI -- Deep Learning for General Image Reconstruction -- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data -- Real-time Video Denoising in Fluoroscopic Imaging -- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution -- Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks. 330 $aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics,$x3004-9954 ;$v12964 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.31 702 $aHaq$b Nandinee 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910502618603321 996 $aMachine Learning for Medical Image Reconstruction$91912511 997 $aUNINA